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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development
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Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

机译：全幻灯片图像上神经母细胞瘤的计算机辅助预后：基质发育的分类

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We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB, According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based oil texture features extracted Ming co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offline feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%. (C) 2008 Elsevier Ltd. All rights reserved.
机译：我们正在开发一种用于神经母细胞瘤（NB），神经系统癌症和影响儿童的最恶性肿瘤之一的计算机辅助预后系统。组织病理学检查是NB常规临床诊断中进一步治疗计划的重要阶段，根据国际神经母细胞瘤病理分类（Shimada系统），根据组织形态将NB患者分为良好和不利的组织学。在这项研究中，我们提出了一种图像分析系统，该系统可对H＆E染色的全玻片NB组织样本进行数字化处理，并根据Schwannian基质发育程度将每个玻片分类为富基质还是贫基质。我们的统计框架执行基于分类的油质地特征提取的明共现统计和局部二进制模式。由于数字化的全幻灯片图像具有高分辨率，因此我们提出了一种多分辨率方法，该方法可模拟病理学家的评估，从而使图像分析从最低分辨率开始，并在必要时切换到更高的分辨率。我们采用了离线功能选择步骤，该步骤确定了训练步骤中每个分辨率级别上最具区别性的功能。修改后的k近邻分类器用于确定分类的置信度，以特定的分辨率进行决策。所提出的方法在43个全幻灯片样本上进行了独立测试，总体分类精度为88.4％。 （C）2008 Elsevier Ltd.保留所有权利。

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